Shirley, MA
Shirley wildfire risk explained
USFS scores Shirley at the 41st national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 638 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Shirley's burn probability — fire likelihood with no building count factored in — sits at the 37th percentile nationally.
What "at risk" means for the buildings here
Most of Shirley's buildings (83.9%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
Shirley against the rest of the country
Shirley's 96th-percentile standing inside Massachusetts outpaces its 41st national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Shirley ranks 18,590 for wildfire risk (1 is highest) and 15,359 by building count (1 is largest). Within Massachusetts alone, it ranks 10 of 248 places by risk. See the full county-by-county picture for Massachusetts on its state page.
Shirley and the insurance market
At the 41st national percentile, Shirley rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Hardening a home in Shirley
Shirley's 83.9% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Shirley's figures come from
The methodology guide shows exactly how USFS turned 638 counted buildings into the percentiles shown above for Shirley. The exposure-zones guide covers what Shirley's dominant indirect exposure actually means, with real examples from across the dataset.